I examensarbetet undersöks metoder för anomalidetektion i tidsserie data. Givet data för overnight index swaps (SEK), så har syntetiskt data skapats med olika ty-per av anomalier. Jämförelse mellan algoritmerna Isolation forest och Local outlierfactor görs genom att mäta respektive prestande för de syntetiska dataseten mot Accuracy, Precision, Recall, F-measure och Matthews correlation coefficient. / In the thesis, methods for anomaly detection in time series data are investigated. Given data for overnight index swaps (SEK), synthetic data has been created with different types of anomalies. Comparison between the Isolation forest and Local outlier factor algorithms is done by measuring the respective performances for the synthetic data sets against Accuracy, Precision, Recall, F-Measure and Matthews correlation coefficient.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-173378 |
| Date | January 2020 |
| Creators | Kuo, Jonny |
| Publisher | Umeå universitet, Institutionen för matematik och matematisk statistik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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